543 results about "Risk identification" patented technology

This invention discloses a securities trading risk control system, method, and device. The system acquires multi-source data in real time, including market data, public opinion, financial reports, industry cycles, government subsidies, executive backgrounds, and liabilities. It generates a sell unlock threshold through a dynamic benchmark return model, triggering mandatory manual confirmation on the client side when the profit rate reaches the target. In parallel, a risk identification module calculates a dynamic anomaly threshold X = industry volatility × 3 + market sentiment coefficient, and independently determines whether the stock price volatility exceeds the threshold, the industry is in a downward trend, there are losses with dividends, there is government / business dependence, there is a high debt ratio, or there is insolvency. Securities that meet these criteria are added to a gray list with tiered warnings and are prohibited from purchase. Securities with falsified financial statements are permanently added to a blacklist and prohibited from purchase. Daily, the system performs a technical pattern scan on securities excluded from the black and gray lists, automatically generating a red list based on bullish moving average alignment and MACD golden cross divergence conditions. The system pushes the blacklist, gray list, and red list data to the client APP in real time. The client directly displays the list status through a built-in rendering engine: blacklist securities are rendered with black labels, graylist securities with gray labels, and red list securities with red labels. Other securities are displayed normally. Users can intuitively view the classification status of each security on the APP interface. This invention solves the technical problems of existing trading systems lacking automatic risk interception and high-quality target identification capabilities by using automated data acquisition, dynamic threshold algorithms, forced interface control, multi-period technical indicator fusion, and blockchain notarization.

This application discloses a method and system for predicting reservoir bank landslides induced by reservoir impoundment based on the principle of refraction. The method includes: obtaining the natural slope angle above water in the natural state and the stable slope angle underwater in the saturated state of the target reservoir bank slope; introducing and calculating the refractive index to characterize the degree of difference between the natural slope angle above water and the stable slope angle underwater; the introduction of the refractive index is based on the understanding of landslide mechanisms: during reservoir impoundment, the water surface acts as an interface for abrupt changes in the physical and mechanical properties of the soil and rock mass, leading to a discontinuous reconstruction of the slope's stable morphology with the water surface as the boundary. The reservoir impoundment process follows the principle of mass conservation, and the actual inclination angle of the failure surface, instead of the natural slope angle above water, better reflects the mechanical state of the slope during instability; based on the value of the refractive index, the instability risk and progressive failure range of the target reservoir bank slope during reservoir impoundment are qualitatively or quantitatively predicted. This application improves the early risk identification and dynamic warning capabilities for reservoir bank landslides induced by impoundment.

The present application belongs to the technical field of intelligent wearable devices, and provides a balance risk early warning method for wearable glasses, which comprises: collecting real-time head movement data of a current period by a head inertia sensor arranged in a glasses leg, and performing step frequency availability determination based on a reference benchmark constructed based on historical head movement data. Through multi-dimensional consistency evaluation and in combination with a dynamic reference benchmark established based on personal historical data of a user, stable periodic gait and a mobile state with weakened step frequency characteristics can be accurately distinguished, and through a three-level collaborative analysis system of rapid screening, deep compensation and fine review, balance risk early warning is realized, which is especially suitable for daily life monitoring of middle-aged and elderly groups and people with decreased balance function, and can still maintain effective risk identification ability in complex scenes such as slow walking, turning, standing and daily activities, which are prone to failure in traditional methods, and can realize daily health monitoring and reminding, thereby providing a solution for preventing falls and ensuring user safety.

This invention discloses an intelligent alarm and control method for personnel safety in power plants, relating to the field of power industry safety control technology. The method includes: acquiring multimodal sensing data related to personnel based on the existing sensing system of the power plant; using a multimodal analysis model to fuse and analyze the collected multimodal sensing data to identify personnel safety risks; generating an alarm trigger signal when the risk level reaches a preset threshold; combining intelligent agent technology to generate a personalized alarm strategy after receiving the alarm trigger signal, simultaneously finding the nearest alarm device based on the real-time location of the personnel for continuous alarm, and establishing a continuous monitoring mechanism to track personnel status in real time; continuously monitoring the behavior and location changes of the alarmed personnel until the risk is confirmed to be completely eliminated. This invention enables cross-system correlation analysis, improves the accuracy of risk identification, effectively shortens response time, and improves the safety level of power plants.

This application relates to the field of network situational awareness technology, and discloses a method and device for network security situational awareness. The method includes: identifying regulatory entities and collecting abnormal events; identifying interactive and operational behaviors as risk characteristics; constructing a first risk identification model and deploying it on an edge terminal; collecting the type and sensitivity attributes of the regulatory entities; dynamically forming regulatory assistance pairs based on the attributes; generating a second risk identification model by weighted averaging of the parameters of the first model based on the regulatory assistance pairs and deploying it in the cloud; real-time interactive data is preferentially screened by the cloud-based second model; if a risk is identified, a structured report is generated and distributed; if not identified, it is reviewed by the first model at the edge terminal. This application achieves distributed intelligent perception with cross-domain collaboration and data not leaving the domain, solving the model bias and centralized delay problems caused by insufficient samples of a single entity, and significantly improving detection accuracy, response efficiency, and collaborative defense capabilities in low-sample scenarios.

This invention provides a user profiling and adaptive transaction permission control method for duty-free e-commerce. It belongs to the fields of big data processing and artificial intelligence technology. The invention includes: collecting user status and operation data, generating a fusion feature matrix through feature fusion; injecting this matrix into heterogeneous graph nodes to construct a dynamic graph, and mining hidden group topological features through a graph attention mechanism; inputting the topological features into a large model to deeply mine the intent of proxy purchasing, and outputting a comprehensive user risk confidence score; constructing an environment-aware threshold adjustment center to dynamically adjust risk control thresholds; matching the comprehensive risk confidence score with the dynamic thresholds to execute tiered permission control. This invention achieves the synergy of big data and a large model, enabling accurate identification of hidden proxy purchasing, reducing false positives while ensuring risk control, balancing security and revenue, and realizing user risk identification and permission control in duty-free e-commerce.

The present application is applicable to the technical field of artificial intelligence. Provided are a method for training a driving risk recognition model, and a driving risk recognition method and apparatus. The method comprises: acquiring data to be processed of a plurality of drivers; determining corresponding events on the basis of the data to be processed; textualizing identifiers of the drivers and the events corresponding to the drivers to obtain textualization results; and on the basis of the textualization results, training a driving risk recognition model to be trained to obtain a trained driving risk recognition model. The driving risk recognition model to be trained is a large language model, and the trained driving risk recognition model is used for outputting a driving risk. The model trained by means of the above method is more accurate.

This application discloses a network access control system, method, apparatus, and device. The system involves a requesting device sending a network access request to a server; the server authenticating the request; if the authentication is successful, allowing the requesting device to access the target network; acquiring information related to a first network access behavior corresponding to the request and information related to a second network access behavior corresponding to the holder of the network access credential; generating a network access risk assessment prompt based on the first and second network access behavior information; the large language model performing a network access risk assessment based on the prompt; and controlling the requesting device's access to the target network based on the network access risk assessment result. This approach effectively balances a high level of user experience and new risk identification capability with low hardware requirements and low risk assessment data processing requirements.

The application relates to a kind of port entry and exit special goods risk identification methods based on knowledge graph construction and entity linking, comprising: according to the risk identification demand of port entry and exit special goods in advance, constructing hierarchical risk factor knowledge graph;Obtain the declaration material of risk to be identified, carry out entity extraction from the declaration material, retrieve historical similar cases according to the extracted entity, build enhanced context containing declaration material and its historical similar cases, and carry out overall vectorization processing;Adopt three-granularity matching strategy: first, through Top-K screening, to match special goods subclass with coarse granularity;Then through threshold filtering, to match risk factor with medium granularity, finally, fine-grained matching risk feature node, to obtain the risk identification result of port entry and exit special goods.Compared with the prior art, the present application has the advantages of more precise risk feature expression, explicit risk knowledge audit, and realizes accurate matching from declaration material to knowledge graph node.

The present application relates to the technical field of electric tool management, and specifically relates to an intelligent management system for electric tools, comprising a device multi-dimensional state acquisition and characteristic vector construction module, which acquires operating parameters and constructs a characteristic vector through an MCU; a tool health assessment and risk identification module, which outputs a health score and an early warning level and identifies a modification behavior; a self-adaptive OTA upgrade and configuration strategy recommendation module, which triggers an update when an anomaly or modification occurs; an electronic fence and disconnection locking module, which tracks a position and triggers locking when crossing a boundary or being disconnected; and a smartphone terminal control response module, which displays a state and issues a control instruction. The present application constructs a closed-loop system of intelligent management and safety cooperation of electric tools through device multi-dimensional state sensing, health scoring and modification identification, self-adaptive upgrade and configuration recommendation, spatial behavior safety locking and user interaction feedback mechanisms.

This specification discloses an intelligence risk identification method, apparatus, and electronic device based on artificial intelligence, comprising: acquiring multi-source heterogeneous intelligence data to be analyzed; fusing the multi-source heterogeneous intelligence data to be analyzed to obtain fused intelligence data; performing semantic understanding on the fused intelligence data to obtain a semantic feature vector of the multi-source heterogeneous intelligence data to be analyzed; fusing the semantic feature vector and the knowledge feature vector of the multi-source heterogeneous intelligence data to be analyzed to obtain a fused feature vector; and inputting the fused feature vector as input to a pre-trained intelligence risk identification model to obtain an intelligence risk identification result, wherein the pre-trained intelligence risk identification model is trained based on a CNN-LSTM model.

The application relates to the technical field of robot posture control, and discloses a robot posture intelligent control method and system based on a self-organizing network communication architecture, which comprises the following steps: acquiring multi-dimensional environment initial data and robot current motion state data; performing multi-source data integration and key feature extraction operations according to the multi-dimensional environment initial data to obtain a fused environment feature set; performing risk identification and posture adjustment strategy generation operations according to the robot current motion state data and the fused environment feature set to obtain a preliminary posture adjustment strategy signal; and performing parameter optimization and secondary calibration operations on the preliminary posture adjustment strategy signal to obtain a final precise posture control instruction. The method can realize precise posture control of a robot in a complex dynamic environment and improve work safety and efficiency.

The application provides a distributed photovoltaic grid-connected intelligent scheduling operation and maintenance method, obtains power generation output data of a photovoltaic access point and power grid voltage data of surrounding nodes, adopts a data analysis method to reveal voltage change trends, simulates voltage rise amplitude when photovoltaic output suddenly increases in combination with power grid interconnection structure characteristics, identifies a risk area if the amplitude exceeds a threshold value, extracts key node state data and classifies the same, determines a voltage fluctuation propagation path by fusing topological information, simulates the influence of a scheduling instruction on voltage distribution, optimizes a scheduling strategy and performs pre-control simulation, ensures that a voltage stability range meets safety standards, and finally realizes risk identification and mitigation. The application effectively balances photovoltaic power generation output and power grid stability, improves the intelligent scheduling operation and maintenance capability of distributed photovoltaic grid connection, and provides a technical guarantee for safe operation of a power grid.

Embodiments of the present application disclose an investment risk identification method, device, equipment, storage medium and program product, to solve the problem of one-sided and inaccurate investment risk identification results in the prior art. The method comprises: constructing an investment knowledge graph, the investment knowledge graph comprising a plurality of investment entities related to the whole investment process and relationships between different investment entities in the plurality of investment entities; determining a low-dimensional topological representation of the plurality of investment entities according to the investment knowledge graph through topological representation learning; calculating the abnormality degree of the plurality of investment entities according to the low-dimensional topological representation of the plurality of investment entities; and identifying investment risks according to the abnormality degree.

This invention provides an intelligent safety management method and system for boiler walkway gratings, relating to the field of industrial safety technology. The method includes: S1, acquiring in real-time the obstruction signal sequence generated by an infrared beam sensor deployed at the grating inlet and the weight signal sequence generated by a pressure sensor deployed below the grating, and synchronizing the obstruction and weight signal sequences with timestamps; S2, inputting the synchronized obstruction and weight signal sequences into a pre-constructed lightweight spatiotemporal feature extraction network to extract multi-dimensional temporal features representing personnel flow, object presence, and weight distribution; and inputting these multi-dimensional temporal features into a risk fusion inference engine built based on Dempster-Shafer evidence theory. This system can eliminate perceptual ambiguity, overcome decision-making rigidity, and mine data value in the harsh edge environment of boiler walkways, achieving progressive intelligent safety management from precise risk identification to dynamic and flexible assessment and early hazard insight.

The present application relates to a kind of construction safety risk early warning method and system based on multi-source data fusion, first acquisition including positioning trajectory, sensor signal, inspection text and video features and so on multiple types of real-time data, in combination with the construction field knowledge graph of semantic consistency verification, realize unified space-time datum and semantic association mapping;Through multimodal feature enhancement, space-time coding and cross-modal fusion, high-dimensional hidden state sequence is established;Causal structure model is constructed using knowledge graph weak causal relationship edge, and counterfactual disturbance response modeling is carried out on high-dimensional data, and the association of context and risk event is extracted;Dynamic adjustment risk assessment result, in combination with risk threshold realizes closed loop early warning response, and through feedback self-optimizing model parameter.The present application improves the precision and self-adaptive ability of construction site safety risk identification and response.

This application relates to the field of tunnel construction safety monitoring technology, and discloses an intelligent identification method for water seepage risk at the tunnel face in water-rich clay layers. This method controls an active infrared excitation module to project radiation in an oblique incidence manner, uses polarization imaging to acquire sequences and calculates Stokes vectors; calculates the degree of linear polarization based on the Stokes vectors and performs threshold segmentation to generate a water film distribution mask; uses the Stokes vectors to construct a polarization angle field reflecting local normal changes as texture features, solves the optical flow constraint equation to obtain the instantaneous velocity field; performs frequency domain transformation on the velocity field, extracts the proportion of low-frequency creep energy through power spectral density analysis, and determines the soil rheological softening factor; finally, combines the water film distribution, rheological softening factor, and instantaneous velocity field to construct a mudslide risk index. This invention utilizes polarization texture reconstruction to solve the problem of weak texture tracking and eliminates elastic vibration interference through frequency domain energy decoupling, effectively improving the accuracy of mudslide risk identification.

The application discloses a thyroid cancer gene detection report intelligent risk assessment method, and relates to the technical field of biomedical information processing, and comprises the following steps: risk conclusion extraction is carried out on obtained multiple thyroid cancer gene detection reports, and the risk conclusion in each detection report is arranged in order of detection time, the time interval between adjacent detection reports is marked, and continuous risk change records are formed. Through time sequence processing and trend reversal identification, the application realizes dynamic analysis of risk assessment, can capture reversal signals in the early stage of risk increase, and improves the timeliness and reliability of risk identification. By setting a reference point and a risk control entrance, combining time interval insertion and detection frequency improvement, the application realizes adaptive correction and continuous updating of risk results, and enhances the real-time performance and safety of risk assessment.

This invention relates to the field of watershed risk management, specifically to a watershed dual-threshold collaborative management method based on big data processing. The method includes: first, acquiring multi-source monitoring data and generating an event state set; then, constructing a dynamic risk propagation network and extracting propagation characteristics, recovery characteristics, and the effectiveness of management measures; further determining the environmental risk threshold boundary, the watershed climate resilience threshold boundary, the dual-threshold state, and the cumulative resilience loss; determining the propagation blocking minimum cut set and the recovery control minimum set based on counterfactual simulation, generating collaborative management instructions, and executing single-layer updates. This invention can improve the continuity of watershed risk identification, the targeting of management actions, and the stability of feedback correction.

This invention relates to the field of data processing and discloses a method and system for project risk identification, early warning, and control. The method includes: acquiring state parameters of logical state nodes in a multi-dimensional data logical network, constructing state evolution trajectories, and calculating decay acceleration within adjacent time windows; identifying parallel monitoring node pairs sharing a common underlying resource pool and calculating the correlation of decay acceleration; when the correlation is negative and the difference exceeds a threshold, generating directed edges pointing to asymmetric resource offset occupancy in the topology graph and outputting early warning and control instructions. This invention identifies the asymmetry in the decay rate of evolution between parallel nodes, constructs asynchronous decay scissor difference judgment rules, achieves reverse mapping of resource scheduling imbalance, eliminates the blind spots of cross-path resource dependency perception in traditional monitoring systems, locks down the risk source before local fluctuations trigger global cascading failures, and improves the timeliness of early warning.

The application provides a supply chain asset isolation and disposal system based on an open API and a blockchain, the system comprising a trust agreement signing module, an asset registration and trust management module, a risk triggering and ownership transfer module, a disposal execution module and a blockchain storage and audit module; the trust agreement signing module realizes electronic agreement signing and blockchain storage; the asset registration and trust management module registers the assets after they meet the inclusion conditions and uniformly manages the trust asset list and state; the risk triggering and ownership transfer module triggers automatic asset ownership transfer based on the risk determination result when a risk event is detected; the disposal execution module executes disposal operations after the ownership transfer and generates disposal results; the blockchain storage and audit module stores key operations to support full-process audit tracing; the application realizes accurate risk identification through multi-source API data fusion and quality evaluation, significantly improving the intelligent level and risk control capability of supply chain asset management.

This invention discloses a method and system for evolutionary identification of corporate tax-related risks based on reinforcement learning, belonging to the field of corporate tax-related risk identification technology. It includes: collecting corporate financial data and extracting state vectors; establishing multiple verification actions and strategies, obtaining verification results and establishing a sample set through these actions; calculating the risk score of the verification results and merging them into the samples, identifying verification results with risk scores reaching a threshold as output results; optimizing the verification strategies; and verifying the output results, removing samples corresponding to verification results from the sample set if they do not meet the standards. This invention, by establishing multiple verification actions, divides the verification operation into various different actions, reducing the computational load of individual verification actions. By using a deep Q-network to determine the selectivity of verification actions and tracing the source of tax-related risks through individual verification operations, it can provide an accurate review scope for manual review and improve verification efficiency.

This invention relates to a multimodal AI-based safety early warning system and method for bulk material storage yards, belonging to the field of port bulk material storage yard safety management technology. The method includes: a multimodal data acquisition subsystem acquiring multimodal sensor data of the target storage yard; an AI risk identification engine fusing and processing the data to determine the risk identification result; a cascaded risk prediction module inputting the risk identification result of the target storage yard into a pre-constructed risk propagation network to obtain a cascaded risk prediction report; a decision-making module generating an optimal emergency response strategy based on the cascaded risk prediction report, the current available resource status, and a preset multi-objective optimization algorithm; an automatic response execution module executing the strategy; and a human-computer interaction module displaying data and results in real time. This application achieves the effect of accurately identifying risks in bulk material storage yards, predicting cascaded risks, and generating optimal emergency response strategies through multimodal data fusion and multi-model collaboration, thereby improving the safety and emergency response efficiency of bulk material storage yards.

This invention discloses a deep learning-based intelligent risk assessment method and system for cross-border trade, relating to the fields of artificial intelligence and cross-border trade technology. The method includes the following steps: S1: Constructing a dynamic causal risk graph, identifying risk transmission paths and labeling their timeliness using a causal discovery algorithm; S2: Processing text, images, and structured data using a cross-modal progressive fusion architecture, aligning the semantic space through cross-modal comparative learning. This invention accurately depicts risk transmission paths by constructing a dynamic causal risk graph, designs a cross-modal progressive fusion architecture to solve the problem of aligning multi-source heterogeneous data, combines a small-sample risk prototype network with adversarial domain adaptation technology to overcome the bottleneck of rare risk identification, and innovatively introduces an interpretable dual-channel output mechanism to meet regulatory compliance requirements. The system has streaming adaptive update capabilities, significantly improving the accuracy, real-time performance, and reliability of risk assessment, providing an intelligent risk control solution for cross-border trade.

The application relates to a battery cell risk assessment generation system, a method and a readable storage medium, wherein the battery cell risk assessment generation system comprises the following steps: extracting multi-dimensional risk features of each single battery cell in a battery cluster based on historical time-series operation data of the single battery cell by a risk calculation module, and determining a corresponding risk value based on the multi-dimensional risk features; performing vector embedding on the original risk data of each single battery cell by a vector embedding module to obtain a semantic vector of each single battery cell, and storing the semantic vector in a vector database; and based on a risk query request, searching in the vector database by an evaluation generation module to obtain a search result, and generating a natural language report of the risk query request based on the search result, thereby improving the accuracy of battery cell risk assessment, combining risk data with a knowledge base, and realizing automatic overvoltage risk identification and risk explanation of single battery cells.

The invention discloses a casing deformation risk identification method, system and device, a storage medium and a program product, and the method comprises the steps: respectively calculating a crustal stress state value Q of a horizontal well and a maximum and minimum principal stress difference value delta sigma max based on a three-dimensional stress result of horizontal well logging interpretation; obtaining a casing deformation dynamic variation coefficient D of the horizontal well according to the crustal stress state value Q and the maximum and minimum stress difference value delta sigma max; and based on the casing deformation power variation coefficient D, determining a casing deformation risk threshold value delta Da of the horizontal well, and further obtaining a casing deformation risk coefficient R of the horizontal well so as to identify a casing deformation risk point of the horizontal well. According to the invention, accurate identification of the horizontal well casing deformation risk point is realized.